Conventional radiographic devices for security applications use X-ray or gamma-ray radiation to form images of scanned objects. If a single radiation energy is used, or a polychromatic source is used with a single detector system than cannot resolve the energy of the transmitted X-rays, only information about the density of the object is obtained. The image may be displayed using a gray-scale or a false-colour palette, but the choice of colour serves purely to enhance the visibility of subtle changes in density and does not convey information about object composition.
Dual-energy X-ray imaging systems are well know, employing either dual X-ray sources or dual X-ray detector systems that respond differently to different X-ray energies. By comparing the transmission of high and low energy X-rays, information about material composition can be inferred in addition to density. Images are typically presented using a fixed colour scale, with blues representing metals, greens mixed materials and browns organic substances. The main drawback of dual-energy X-ray systems is the limited penetration of lower energy X-rays, which limits their use to thinner and lighter objects such as packages and luggage.
Sowerby and Tickner [1] describe an imaging system using a combination of gamma-ray and fast neutron radiography to form density and composition images of thick objects such as air and sea cargos. They also describe a simple means of displaying images from this system, with composition mapped to colour hue and density mapped to colour brightness. However, this display system fails to make best use of the differences between the neutron and gamma-ray images to extract maximum information about the object being scanned. In particular, it does not handle the imaging of thick, highly attenuating cargo where one of either the neutron or gamma-ray radiation beams is totally blocked. Similarly, it fails to properly take into account the different noise levels on the neutron and gamma-ray images.
Noise removal from a given image is an important practical problem occurring in many applications, particularly in nuclear imaging processing.
Simple smoothing techniques of averaging pixels over their immediate neighbourhood is well known. However the main drawback of such a method is the loss of the image details by averaging. More recent methods for noise removal, such as bilateral filtering [2,3] which use a Bayesian approach as their theoretical foundation, have concentrated on preserving image detail whilst removing noise. It has been shown that the bilateral filter method is very effective in removing additive noise from images and it is simple to implement and does not require iteration.